(Optional) Exporting the Quantized Model to ONNX - 3.5 English

The following code shows how to perform post-training quantization and export the quantized model to ONNX with vai_q_tensorflow2 API:

model = tf.keras.models.load_model('float_model.h5')
from tensorflow_model_optimization.quantization.keras import vitis_quantize
quantizer = vitis_quantize.VitisQuantizer(model)
quantized_model = quantizer.quantize_model(calib_dataset=calib_dataset, 
                                           output_format='onnx',
                                           onnx_opset_version=11,
                                           output_dir='./quantize_results',
                                           **kwargs) 

output_format

String. Indicates the format to save the quantized model. Options are:

• '' for skip saving
• h5 for saving .h5 file
• tf for saving the saved_model file
• onnx for saving the ONNX file

The default value is ''.

onnx_opset_version

Int. The ONNX opset version. It takes effect only when output_format is 'onnx.' The default value is 11.

output_dir

String. Indicates the directory to save the quantized model. The default value is './quantize_results.'